When I was in school, I always found that the blackboard was the best teaching tool (as opposed to say a pre-prepared static slide in PowerPoint). As a student, I found it really helpful to see a concept get built up from nothing, and accordingly when I was a teaching assistant in economics, I also preferred using the blackboard to build up concepts together with my students (I also found it more fun as it was now a collaborative experience). Now in the business world, developing concepts is still as important as when I was in school, but using a blackboard will likely get me… looks… Luckily, there’s a very neat alternative: the animation!

Dataset

This post will be more on creating animations, rather than focus on the specific dataset being animated. Having said that, an interesting dataset will make this all the more exciting.

Lately in Canada, there has been some concern over the number of babies the country is producing. We shall then take a look at how income per person has been affecting the number of children women have given birth to throughout the years across all regions around the world.

Pulling in the data

We’ll begin with downloading the data.

I’m going to do some data cleanup but I will skip the code to keep this post more brief.

[…data cleanup…]

After the data cleanup, we will join all the datasets together for one unified set:

# Join datasets
data.join <- left_join(data.income.clean, data.babies.clean) %>%
        left_join(data.pop.clean) %>%
        left_join(data.region.clean)

data.join[complete.cases(data.join),] -> data.join

Here’s a random sample of what 10 data points look like:

country	year	income	babies	pop	region
Ecuador	1971	5866	6.01	6248831	Latin America & Caribbean
Uzbekistan	1956	2133	6.38	7947735	Europe & Central Asia
Denmark	1992	34152	1.76	5171304	Europe & Central Asia
Kyrgyz Republic	1987	3493	4.04	4179484	Europe & Central Asia
South Africa	1954	7623	6.26	15019626	Sub-Saharan Africa
Djibouti	2008	2502	3.76	809639	Middle East & North Africa
Belarus	2010	15703	1.46	9492122	Europe & Central Asia
Greece	1994	21147	1.34	10542970	Europe & Central Asia
Dominican Republic	1974	4306	5.38	5019471	Latin America & Caribbean
Moldova	1994	2620	1.98	4357283	Europe & Central Asia

Method 1: `gganimate` with `tweenr`

First up, is a combo-solution that directly extends the ggplot2 universe: the package gganimate developed by David Robinson (@drob) along with the package tweenr developed by Thomas Lin Pedersen (@thomasp85).

Let’s develop this animation.

library(gganimate)
library(tweenr)

# Tween for smoother animations
data.join.tween <- data.join %>%
        rename(x = income,
               y = babies,
               time = year,
               id = country) %>%
        mutate(ease = "linear") %>%
        select(-region) %>%
        tween_elements("time", "id", "ease", nframes = 1000)

# Re-add prior data
data.join.tween <- inner_join(data.join.tween, 
                              data.region.clean, 
                              by = c(".group" = "country")) 

# Plot
p <- ggplot(data.join.tween, aes(x = x, y = y)) +
        geom_point(aes(size = pop, frame = .frame, colour = region), 
                   alpha = 0.7) +  
        xlab("GDP per capita") +
        ylab("Number of Babies Born per Woman") +
        theme_minimal(base_size = 16) +
        geom_smooth(aes(group = .frame, frame = .frame), method = "loess", 
                    color = "black", linetype = "dashed", 
                    se = F, size = 0.5) +
        theme(legend.position="none") +
        scale_x_log10(labels = dollar) + 
        scale_size_area(guide = FALSE, max_size = 20) +
        scale_color_brewer(name = "", palette = "Set2") +
        facet_wrap(~region)


# Animate Plot
# gganimate(p, title_frame = T, interval = 0.02, "../../static/img/gganimate.gif", 
#           ani.width = 800, ani.height = 800, 
#           ani.res = 90) #<- Not run to save render time

Voila, a pretty nice looking animated graph if I may say so myself 😃.

Method 2: `plotly`

The second method is via the plotly package, developed by a Canadian company by the same name.

Plotly also has a function which allows you to translate a ggplot2 graph into a plotly graph which we will use below.

# Generate base ggplot2 graph
p2 <- ggplot(data.join, aes(x = income, y = babies)) +
        geom_point(aes(size = pop, frame = year, colour = region, group = country), 
                   alpha = 0.7) +  
        xlab("GDP per capita") +
        ylab("Number of Babies Born per Woman") +
        theme_minimal(base_size = 10) +
        geom_smooth(aes(group = year, frame = year), method = "loess", 
                    color = "black", linetype = "dashed", se = F, size = 0.5) +
        theme(legend.position="none") +
        scale_x_log10(labels = dollar) + 
        scale_size_area(guide = FALSE, max_size = 20) +
        scale_color_brewer(name = "", palette = "Set2") +
        facet_wrap(~region)

# Create plotly graph
ggplotly(p2, height = 800, width = 800) %>%
        animation_opts(frame = 300,
                       easing = "linear",
                       redraw = FALSE)

Conclusions

Looking at the data, I found it particularly interesting to see China (the biggest ball in the East Asia region) bounce around so much, and goes to show how drastic the Great Famine was in 1959 - 1961, and also how effective the One Child Policy was after it was introduced in 1979.

Outside of those shocks to China, it seems that for the most part the number of babies born had a distinct negative relationship with the amount of income for all regions except Africa. Maybe there is some cultural explanation there?

As for the animations themselves, I find that these are two good methods for creating animations, both online and offline. I tend to prefer the plotly solution if I’m posting to a website. The vector graphics and built-in smoothing are very nice touches by Plotly.

Dataset

Pulling in the data

Method 1: gganimate with tweenr

Method 2: plotly

Conclusions

Method 1: `gganimate` with `tweenr`

Method 2: `plotly`